Estimation of Atmospheric 3rd Line Diesel Oil Solidifying Point via Adaptive Kernel Based Relevance Vector Machine

摘要：

Atmospheric 3rd line diesel oil solidifying point is an important quality index, which cannot be measured in real time, in petroleum industry. Due to the great nonlinear characteristic of distillation columns, common statistic methods, such as PCR and PLS, based on linear projection, are not able to estimate such a quality index effectively. In this paper, Adaptive kernel based Relevance Vector Machine (aRVM) is introduced to build a nonlinear soft sensor model. This soft sensor is then applied to a real solidifying point estimation experiment, with comparison to other nonlinear models such as KPLS, SVM and typical RVM. The result reveals that aRVM shows better performance than KPLS, SVM and models a much sparser representation than SVM and typical RVM.

作者: Yong Tao Yongheng Jiang Dexian Huang

作者单位: Process Control Engineering,Department of Automation, Tsinghua Univ., Beijing 100084

会议类型: 国际会议

会议名称: 2011 International Symposium on Advanced Control of Industrial Processes(2011工业过程先进控制技术国际研讨会)

会议地点: 杭州

会议语种:英文

页码: 530-534

在线出版日期: 2011-05-01（万方平台首次上网日期，不代表论文的发表时间）